1. Field of the Invention
This invention relates to an image sharpness calculation method and apparatus for calculating a sharpness of an image in a television receiver, a scanner, a facsimile, a printer or a like apparatus and an image sharpening apparatus for adjusting the sharpness of an image in response to a thus calculated sharpness of the image.
2. Description of the Related Art
One of methods employed to calculate a sharpness of an image is calculation of an acutance. The output of a system when a step function is inputted to the system is called edge spread function (ESF), and according to the method, an edge spread function of an object image is calculated first. Where the edge spread function is represented by h(x), the acutance A is represented by the following equation (1): ##EQU1## where a and b are an upper end and a lower end of an edge of the output, respectively.
Meanwhile, several methods for caculation of a sharpness are based on a magnitude transfer function (MTF) of an object image. According to "Objective Evaluation System of Display Image Sharpness", Bulletin of Thesis of the Electric Communication Society of Japan, Vol. J70-D, No. 2, 1987, pp.474-481, the methods are generally represented by the following equation (2): ##EQU2## where f is a spatial frequency, n is a positive integral number (normally 1 or 2), R(f) is a magnitude transfer function of the entire system, and E(f) is a magnitude transfer function of the vision.
In short, a sharpness is determined as a value obtained by normalizing an integral value of a transfer function of a system with an integral value of a transfer function of the vision.
Further, one of methods of sharpening an image is unsharp masking.
The unsharp masking is a process of multiplying a difference of an unsharp image f.sup.- from an original image f by a constant and adding the product to the original image f. The process is represented by the following equation (3): EQU fs=f+k(f-f.sup.-) (3)
f-f.sup.- in the equation (3) above represents a high frequency component of the image since a low frequency component of the image is subtracted from the image. Therefore, the unsharp masking is a method of sharpening an image by multiplying a high frequency component of the image by a constant and adding the product to the original image. Generally, sharpening signifies emphasis of a high frequency component, and if the equation (3) is generalized as a sharpening method, where a high frequency filter is represented by gs, the unsharp masking is represented by EQU fs=f+k(f*gs) (4)
where * represents a convolution. This applies similarly in the following description.
An improvement in the unsharp masking is disclosed, for example, in Japanese Patent Laid-Open Application No. Heisei 3-278284 wherein the sharpness is emphasized in response to the output of an edge detection filter which only detects an edge of an image. In the image sharpness emphasis method, the edge detection filter is applied to a neighboring region of each picture element. Then, the coefficient k of the equation (3) regarding the picture element is calculated as a function of the output value of the edge detection filter. In short, the value of the coefficient k is varied for each picture element in accordance with a situation in the proximity of the picture element thereby to prevent possible deterioration of the picture quality which may be caused by emphasis of the graininess at a flat portion of the image.
However, the conventional image sharpness calculation method is disadvantageous in that the sharpness cannot be measured unless an edge spread function or a magnitude transfer function of an object image is known. And, a considerable number of steps are required for measurement of such function. Further, the conventional image sharpness calculation method cannot be applied to an image whose nature of deterioration is quite unknown.
Meanwhile, the conventional unsharp masking is disadvantageous in that, in order to accurately adjust two parameters including a high frequency component (f*gs in the equation (4)) to be emphasized and a coefficient (k in the equation (4)) for multiplication by a constant in response to the degree of the unsharp condition of an image, normally an operator must visually confirm several images one by one to find out optimum values for the two parameters.
This is because, since sharpening is performed in order to improve the picture quality of an image, when the degree of the unsharp condition of the image is, for example, considerably high, the coefficient k must be a high value, but on the contrary when the degree of the unsharp condition of the image is not considerably high, the coefficient k must be a low value. In short, in order to achieve a high picture quality by means of sharpening, the parameters of sharpening must be varied in response to the degree of the unsharp condition of the image.
The disadvantage Just described resides similarly in the method disclosed in Japanese Patent Laid-Open Application No. Heisei 3-278284. According to the method, the function (which is in the form of a lookup table in the disclosure) of the output of the edge detection filter for calculating the coefficient k for each picture element must necessarily be varied in response to the degree of the unsharp condition of the image. Consequently, the method is disadvantageous in that images may be visually confirmed one by one similarly as in the unsharp masking.
The disadvantages described above do not matter very much if the degree of the unsharp condition of an image to be sharpened is limited to some degree. This is because, since also the optimum values of parameters of sharpening are limited, images need not be visually confirmed one by one to vary the parameters. Further, even if the degree of the unsharp condition of an image to be sharpened is not limited, if the degree of the unsharp condition can be measured numerically, then the parameters of sharpening may be varied in response to the value of the degree of the unsharp condition, and consequently, the disadvantages described above do not matter.
However, images actually inputted to scanners, copying machines and so forth have various unsharp conditions, and it is impossible to restrict the degrees of the unsharp conditions. Further, while, in order to numerically restrict the degree of the unsharp condition, conventionally a mechanism of the unsharp condition of the image (transfer function of the image) must generally be known, the mechanism of the unsharp condition is unknown as regards most images inputted to a scanner, a copying machine or the like.
From the circumstances described above, the conventional methods are disadvantageous in that, in order to sharpen many and unspecific images having different degrees of the unsharp condition to assure a high picture quality, an operator must visually confirm the images one by one by trial and error to determine sharpening parameters.
Further, in order to determine such optimum sharpening parameters, the skill and the experience are conventionally required for an operator, and the conventional methods are disadvantageous also in that it is very difficult for a person who does not have such skill or knowledge to determine optimum parameters.